Virtualizing content

ABSTRACT

Techniques for virtualizing content are disclosed. One or more objects comprising source video content are determined. The one or more objects comprising the source video content are virtualized by mapping each to and representing each with a corresponding database object. Data comprising the corresponding database objects is provided for rendering the source video content instead of any original pixel information of the source video content so that a virtualized version of the source video content is rendered.

CROSS REFERENCE TO OTHER APPLICATIONS

This application is a continuation of co-pending U.S. patent applicationSer. No. 14/069,040 entitled VIRTUALIZING CONTENT filed Oct. 31, 2013which is incorporated herein by reference for all purposes, which claimspriority to U.S. Provisional Patent Application No. 61/720,857 entitledRENDERING DIGITAL CONTENT filed Oct. 31, 2012 which is incorporatedherein by reference for all purposes.

BACKGROUND OF THE INVENTION

Display roadmaps are rapidly transitioning from formats such as Full HD(1920×1080 pixels) to 4K UHD (3840×2160) as a consumer standard. Theindustry is also anticipating changes to even larger formats such as 8KUHD (7680×4320) within the next decade. However, the standards beingdefined for the UHD formats of video (up to 120 frames per second) willchallenge available broadcasting and streaming bandwidth, particularlyfor wireless devices. The standards also challenge the industry'sability to produce input hardware (i.e., camera/video technologies) thatmatches up to the native output capability of the display hardware forthe general consumer. High quality content creation for these newformats is not possible for the common user, and all video contentcaptured prior to the UHD standards will not be natively compatible withdisplay hardware in the near future. That is, the most common onlinecontent can never be viewed as a high quality experience with upcomingdisplay hardware. Furthermore, imagers will lag display quality for theforeseeable future. Regardless, for amateur users,environmental/lighting conditions are typically not ideal for capturinghigh quality content. Moreover, less than ideal timing, shootingproblems, spontaneous events, etc., also often reduce the quality ofcaptured content.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments of the invention are disclosed in the followingdetailed description and the accompanying drawings.

FIG. 1 is a high level block diagram illustrating an embodiment oftypical stages associated with communicating content.

FIG. 2 is a flow chart illustrating an embodiment of a high levelcontent distribution process based on context abstraction.

FIG. 3 is a block diagram illustrating an embodiment of the architectureof the disclosed content distribution platform based on contextabstraction.

FIG. 4 is a flow chart illustrating an embodiment of a process fortransforming source content into a virtualized, computational spacebased representation.

FIG. 5 illustrates an example of camera motion.

FIG. 6 illustrates an example of overlaying a projection of referenceddatabase objects on the camera view of the source scene.

FIGS. 7A-C illustrate examples of overlay methods.

DETAILED DESCRIPTION

The invention can be implemented in numerous ways, including as aprocess; an apparatus; a system; a composition of matter; a computerprogram product embodied on a computer readable storage medium; and/or aprocessor, such as a processor configured to execute instructions storedon and/or provided by a memory coupled to the processor. In thisspecification, these implementations, or any other form that theinvention may take, may be referred to as techniques. In general, theorder of the steps of disclosed processes may be altered within thescope of the invention. Unless stated otherwise, a component such as aprocessor or a memory described as being configured to perform a taskmay be implemented as a general component that is temporarily configuredto perform the task at a given time or a specific component that ismanufactured to perform the task. As used herein, the term ‘processor’refers to one or more devices, circuits, and/or processing coresconfigured to process data, such as computer program instructions.

A detailed description of one or more embodiments of the invention isprovided below along with accompanying figures that illustrate theprinciples of the invention. The invention is described in connectionwith such embodiments, but the invention is not limited to anyembodiment. The scope of the invention is limited only by the claims,and the invention encompasses numerous alternatives, modifications, andequivalents. Numerous specific details are set forth in the followingdescription in order to provide a thorough understanding of theinvention. These details are provided for the purpose of example, andthe invention may be practiced according to the claims without some orall of these specific details. For the purpose of clarity, technicalmaterial that is known in the technical fields related to the inventionhas not been described in detail so that the invention is notunnecessarily obscured.

FIG. 1 is a high level block diagram illustrating an embodiment oftypical stages associated with communicating content, such as image,video, or other multi-media content. At stage 102, source content iscaptured by a camera or other recording device. The format and qualityof the source content is based on the capabilities and limitations ofthe recording device. At stage 104, the source content is processedand/or stored for immediate (i.e., real time) or later distribution. Forexample, the content processing of stage 104 may include contentcompression for easier storage and distribution. In some embodiments, aslater described herein, the content processing of stage 104 comprisescontent conversion into a prescribed computational space. At stage 106,the content is delivered to and rendered at a destination device.

Typically, the quality of rendered content at a destination device isrestricted by and does not exceed the quality of the source contentdespite the rendering capabilities of the destination device. However,the limitations of source content quality pose a growing challenge asrapid improvements occur in the hardware of destination devices. Poorquality source content is not suitable to serve the demands of HD (highdefinition) and emerging beyond-HD capable destination devices. StandardHD is quickly giving way to 2K HD, which will evolve into 4K HD.Moreover, hardware roadmaps already contemplate 8K HD, i.e., UHD (ultrahigh definition), which is approximately sixteen times the format sizeof standard HD.

Techniques for decoupling the format and quality of source content aswell as the format and quality of the same content processed for storageand distribution from the rendering capabilities of a destination deviceare disclosed in detail herein. The disclosed multi-media technologyplatform facilitates flawless quality when rendering content from anarbitrary source on any of a plurality of types of destination devicesby allowing performance up to the capability of the receiving system(including best-in-class display technologies) to be achieved. Thus, ahigh fidelity, cinematic quality viewing experience may be realized fromany image or video source. The disclosed techniques not only allow usersto create visually stunning image and video content anywhere, at anytime, and on any device without having to be an expert in multi-mediacontent creation tools but also allow users to re-imagine content innearly unlimited ways. Although image or video source content isdescribed in many of the given examples, the techniques disclosed hereinmay be extended to and employed with respect to any multi-media content.

FIG. 2 is a flow chart illustrating an embodiment of a high levelcontent distribution process based on context abstraction. At step 202,source content is obtained. For example, image or video content isobtained at step 202 from a device employed to capture the contentand/or previously captured content is obtained from storage. At step204, the content obtained at step 202 is contextualized. For example,one or more elements and/or characteristics of the context of thecontent are identified at step 204. At step 206, the contextualizedcontent is converted to a prescribed, proprietary computational space.For example, elements and/or characteristics of the context of thecontent identified at step 204 are abstracted and transformed intoobject-based representations at step 206. Such object-basedrepresentations may be based on existing object assets residing in adatabase or cloud of assets. Alternatively, a new object representationmay be determined and added to such a database or cloud. In someembodiments, the conversion of step 206 substantially, if notcompletely, virtualizes the source content such that little, if any, ofthe original source content (e.g., pixel and/or other data) is preservedwhen converted to the computational space based representation at step206.

The content is optionally augmented at step 208. In most cases, thecontent is augmented to include at least one or more elements orfeatures that were never a part of the original content obtained at step202 but are included to provide an enhanced viewing experience at adestination device. At step 210, the content is provided to adestination device for rendering. Since the computational space isagnostic to the destination device, a perfect match for the capabilitiesof any destination device can be achieved using process 200. Thus,content may be rendered at a destination device based on the nativeformat of the destination device as well as in the best qualitysupported by the destination device. For example, using process 200,source content captured via a relatively low resolution mobile phonecamera may be rendered at a high resolution destination device incinematic quality. In some embodiments, the content augmentation of step208 may be performed client-side at a destination device.

As described, process 200 facilitates a high fidelity viewing experienceregardless of the format or quality of the source content and offersunprecedented variability within the actually rendered content. Thedisclosed platform does not comprise merely compressing, upscaling,and/or replicating the appearance of source content. Rather, informationgathered from contextual clues from the source content is employed tovirtualize or create a re-imagined version of the content. In mostcases, information is being added to, not removed from, the content.That is, more information than what is present in the original sourcecontent is provided for rendering at an output device. In many cases,the virtualized or re-imagined version of the source content comprisesvery little, if any, of the data comprising the original source content.Moreover, the content may be re-imagined differently for differentoutput devices, for example, based on the rendering capabilities of thedevices and/or other criteria associated with the devices. A keyobjective of the disclosed approach is not to create a lossless versionof the source content, although that may coincidentally be an outcome,but rather to prioritize creation of content best suited for thecapabilities of a destination device and/or customized for thedestination device or a user thereof.

FIG. 3 is a block diagram illustrating an embodiment of the architectureof the disclosed content distribution platform based on contextabstraction. This architecture provides novel techniques for creating,re-imagining, remastering, streaming, and rendering digital content. Thevarious blocks of content distribution platform 300, for example, may beconfigured to perform corresponding steps of process 200 of FIG. 2.Block 302 comprises source content obtained from an input source. Invarious embodiments, the input source may comprise an imaging device(such as a camera) or may comprise existing multi-media content. Block304 comprises a recognition engine configured to identify one or moreelements and/or characteristics of input source content 302. That is,recognition engine 304 contextualizes source content 302. In variousembodiments, recognition engine 304 may be configured to identifyfeatures, objects, background, illumination environment, etc., withininput source content 302 using any combination of one or more standardand proprietary machine vision methods and/or assisted by human input.

Block 306 comprises a mapping engine configured to correlate and map theelements and/or characteristics identified by recognition engine 304 tothe best matches available from an existing database 308 in which objectspecifications comprise prescribed computational space basedrepresentations. In some embodiments, mapping engine 306 is furtherconfigured to determine relative positions and orientations ofidentified objects within the model environment or scene representingthe input source content. In many cases, objects in database 308 mayneed to be morphed or modified through available parameterization tomatch identified objects. In cases in which a suitably close matchcannot be identified, an object may be substituted for by a best guess.Alternatively, an object and its associated properties may be created onan ad hoc basis from an analysis of source content 302 (e.g., byextracting geometry and texture) and submitted into database 308 for useduring matching and mapping. In some cases, the correlation and/ormapping performed by mapping engine 306 is at least in part assisted byhuman input. In many cases, the environment or scene representing theinput source content modeled by mapping engine 306 comprises little, ifany, of the original data comprising input source content 302 since themodeled environment or scene comprises database object basedrepresentations of content. Thus, input source content 302 iseffectively virtualized or re-imagined.

Block 308 comprises a database comprising an archive of available masterobjects and their associated properties. The depth and complexity ofdatabase 308 increases over time as it is populated as more and moredata becomes available. An object entry in database 308 may comprise anyappropriate and applicable parameters for specifying the object. Forexample, an object specification may include data comprising geometry(e.g., shape and size), surface texture, optical response toillumination, and/or any additional properties associated with theobject (e.g., brand name, date and location of origin of manufacture,material properties, etc.). In some embodiments, object properties areparameterized to the extent possible to broaden the class of objectsthat can be represented by a given master object. Database 308 may bepopulated using any one or more appropriate techniques, some examples ofwhich are depicted in FIG. 3. For instance, data comprising database 308may be bootstrapped 310. That is, an object model may be created bydirectly extracting relevant data from input source content 302.Further, data comprising database 308 may be extracted from availablesources 312. That is, object data may be extracted from other existingsources of image and/or video content. Moreover, data comprisingdatabase 308 may at least in part be entered by a user 314. That is,object data may be provided by user input comprising, for example,modeled/simulated data and/or data obtained via direct scanning and/orimaging of physical objects. Database 308 stores data associated withfrequently appearing objects that can be used to generate content andadaptively improves over time as more objects and/or associatedproperties are learned.

Block 316 comprises a content context modification engine configured tomodify and/or customize the environment or scene modeled by mappingengine 306 according to desired performance criteria. For example, themodeled environment may be altered by modifying objects, substitutingobjects with alternatives, and/or removing objects altogether. Moreover,the lighting of the modeled environment (e.g., lighting type andposition, luminance, chromaticity, etc.) may be modified. In addition,perceived performance errors in the modeled environment or scene may beappropriately corrected as applicable. In the case of human subjects(i.e., parameterized master objects representing people in the sourcecontent), for instance, problems in gaze, appearance, etc., may becorrected. Furthermore, alternative camera positions/perspectives may beintroduced into the modeled environment, for example, in addition to orinstead of a default virtual camera appropriately located in a positionto recreate a perspective defined by the source content.

Block 318 represents providing or outputting the modeled environment,for example, to a (requesting) client or destination device at which themodeled environment is desired to be rendered. Instead of originalsource content 302 being provided, parameters defining objectscomprising the model representing source content 302 are delivered. Sucha model environment specification may include, for example, objectidentifications, relative positions, and orientations; background andlighting environment specifications; camera position; etc. In someembodiments, a subset of the object database needed to create themodeled scene is provided. Alternatively, a copy of the subset of theobject database needed to create the modeled scene may already beavailable at the client site or destination device to which the modelenvironment is delivered and thus need not be provided. In someembodiments, block 318 includes storing a specification of the modeledenvironment or scene, e.g., for later distribution.

Block 320 comprises a rendering engine configured to render theenvironment or scene defined by the model at an output device (e.g., aflat panel display) with the characteristics of the objects referencedto the properties provided by the object database. Best in classtechniques from the computer graphics industry may be applied byrendering engine 320 when rendering a scene. The subset of the objectdatabase needed to render the scene may be accessed by rendering engine320 through a local copy or through direct access to database 308. Inmany cases, the scene is rendered by rendering engine 320 with a qualitythat is matched to the maximum quality performance achievable by theoutput device, i.e., is optimally matched to the display format, colorperformance, frame rate, etc., of the output device. Moreover, the scenerendered by rendering engine 320 may be personalized or customized basedon a user and/or profiling of a user of the output device.

The described techniques for content distribution based on contextabstraction offer numerous benefits. A high quality viewing experiencemay be delivered regardless of the quality of the source content andeven when the source content is significantly degraded, e.g., due tocompression artifacts or other noise. In many cases, the source contentonly has to be of sufficient quality for recognition engine 304 toidentify features, objects, and background types. For example, in somecases, six bits of color per pixel in the source content are sufficientrather than thirty bits. Moreover, compression based block artifacts arein most cases acceptable; thus, very high compression ratios viatraditional methods may be used to store the source content.

A scene captured by a common user using a device with consumer gradesensors having limited dynamic range is not only of relatively lowresolution/quality but also is typically filmed without proper lightingor environmental conditions. However, using the disclosed techniques,viewed content can still be very high quality. Once an object isrecognized, local tone correction can be applied, and highlightssaturating an image can be corrected naturally when the scene is relit.Similarly, any other perceived imperfections can be corrected in orremoved from the scene. For example, image stabilization as well as postprocessing to correct for performance errors may be introduced asapplicable. With respect to a human subject, for instance, gaze may becorrected, closed eyes may be opened, stray hair may be removed, etc.

In various embodiments, content may be edited and/or re-imagined withany number of alternative visual qualities (e.g., lighting, surfacecharacteristics, camera perspective, etc.) than originally intended fromthe source, and new and/or different content may be introduced. Forexample, a scene may be re-rendered with an advertiser's content such asbrands, images, products, etc. Moreover, content may be personalized orcustomized based on an end user. Since object models may be specified in3D in database 308, corresponding content may be naturally rendered in3D if desired. Conversely, content may be rendered as a cartoon (e.g.,in anime style) if desired.

For many applications, the disclosed content distribution techniquessignificantly reduce required communication bandwidth, for example,compared to traditional video codec approaches. Ultra-low bandwidth isespecially feasible with fairly static scenes since object motion is theonly required data that needs to be communicated. For example, videoconferencing applications (such as telepresence, video chat, mobilevideo calls, etc.) are particularly well-suited for the disclosedplatform. Source content from a low quality webcam, for instance, may berendered in a UHD enabled telepresence conference room in cinematicquality. In addition to providing an ultra-low bandwidth solution formany applications, the disclosed content distribution techniquesfacilitate cinematic quality content creation at very low cost andoverhead and without requiring expertise by an end user. Existingcontent may be easily re-imagined and/or remastered in limitless ways.For example, any archived image or video source may be renderedbeautifully with cinematic quality on a UHD display. Additionalapplicable fields in which the disclosed techniques may be especiallyrelevant include applications in the gaming, entertainment, andeducation industries as well as in any other industry in which efficientcontent distribution is paramount.

The described content contextualization platform is furthermore amenableto search applications, particularly contextual based image and videosearches. A search may be conducted by searching for something accordingto one or more object definition criteria. Alternatively, a search maybe conducted by creating content that best matches the search criteria.Content may be created, for instance, by modifying something that isquite close already (e.g., by re-lighting, re-orienting, and/or colorcorrection; object removal and/or editing; etc.). Searches may beefficiently conducted against a network of one or more databases (suchas database 308) comprising content libraries of object assets.

The manner in which database objects are specified and stored in thedescribed architecture moreover facilitates future proofing multi-mediacontent libraries with respect to evolving display and viewingtechnologies and standards. Further, content generation in 2D and 3D iseasily supported since many database objects are inherently specified in3D. Additionally, multi-scale object databases may be linked,facilitating support of an “infinite zoom” feature allowing advancedzooming, possibly down to even micro-scale and nano-scale levels.

In addition to the aforementioned advantages and applications, markettrends in display technology are anticipated to favor the disclosedcontent distribution techniques based on context abstraction. Toaccommodate emerging display technologies, a new paradigm for contentdistribution will be imperative. The disclosed techniques provide onesuch architecture that is not only viable and scalable but also economicand resource efficient.

FIG. 4 is a flow chart illustrating an embodiment of a process fortransforming source content into a virtualized, computational spacebased representation. For example, various steps of process 400 may beperformed by recognition engine 304 and mapping engine 306 of FIG. 3. Insome embodiments, process 400 is employed to transform original sourcecontent into a substantially virtualized representation in which little,if any, pixel and/or other data from the original source content ispreserved in the computational space based representation of the sourcecontent, which is eventually provided to a destination device at whichthe source content is desired to be rendered. Although described withrespect to video content, the steps of process 400 may be appropriatelyadjusted for image or other multi-media content.

At step 402, camera pose relative to a scene comprising the sourcecontent is determined. In various embodiments, a scene may besubstantially static and/or may include varying degrees of motioncorresponding to changing camera positions (e.g., if the camera ispanned or otherwise moved to capture different angles or perspectives ofthe scene). From the camera motion, standard computer vision basedfeature tracking algorithms may be employed to compute the pose (i.e.,the position of the camera) relative to the scene. In some embodiments,a sequence of camera poses is determined at step 402 for a camera cutshot, wherein a cut comprises a sequence involving a single continuousmotion of the camera. FIG. 5 illustrates an example of camera motion andspecifically depicts two frames—frame 500 and frame 502—of a sceneduring the same sequence at different scrub times. Bounding box 504 andbounding box 506 indicate the field of view during each frame, andcamera 508 and camera 510 represent the location of the camera positionwithin each frame.

At step 404, key frames are defined. In the given context, a key framecomprises a frame in which critical features and/or objects of the sceneare within the field of view of the camera. Such features and/or objectsmay leave or enter the field of view over time during a sequence.However, key frames may be selected to include most of the relevantfeatures and/or objects needing tracking during a cut shot.

At step 406, objects are identified in each key frame. In some cases,object recognition is at least in part automated, e.g., for human faces,objects with regular geometries, etc. In some cases, object recognitionis at least in part assisted by human intervention. In such cases,points in a frame or scene tracked by standard machine vision basedalgorithms are presented to a human operator for consideration. Thehuman operator indicates or specifies a subset of tracked points thatcorrespond to an object. Additional computer vision algorithms may besubsequently employed with respect to such a human operator identifiedsubset of tracked points to segment a corresponding object from an imageby determining the visual boundaries of the object with respect to otherforeground and background content in the associated frame.

At step 408, each object identified at step 406 is mapped to a databaseobject. In some cases, object mapping is at least in part automated. Insome cases, object mapping is at least in part performed by a humanoperator. For example, a human operator may identify an object type orclass (e.g., by traversing through a decision tree classifying differenttypes of objects) and/or search against the database for an identifiedobject class. In response to such a search, one or more similar objectsmay be presented to the operator for consideration. The operator mayselect one of the available database objects to represent a given objectidentified in the scene. Thus, an object from the source content ismostly, and in many cases completely, virtualized when matched andmapped to a corresponding database object, which is what is eventuallyused during rendering. In some embodiments, instead of seeking a bestmatch, object mapping comprises purposely misinterpreting or mismatchingan identified object when selecting a corresponding database object. Forexample, an identified cloud object in a source video may be mapped to asun object in the database to reflect different weather.

In some embodiments, step 406 comprises identifying a partial object,and step 408 comprises mapping an identified partial object to a wholeor complete database object. Objects and/or space comprising the sourcecontent may be expanded or augmented in the virtual environment or scenemodeled from the source content as applicable. For example, a housecorner identified at the edge of a frame in the source content may bemapped to a complete house object from the database. Thus, the virtualspace comprising the modeled environment or scene may be much largerthan the space encompassed by the original source content since in manycases it is desirable to render whole objects instead of partialobjects, for example, to make the scene more visually appealing andamenable to interaction (e.g., via virtual user entry into the scene andchange of camera position/perspective).

In some embodiments, to properly place an object in a model environmentor scene representing the source content, at least a prescribed numberof non-coplanar points are needed to specify the object. For example, anautomated process and/or human operator may specify or select at leastfour non-coplanar tracked points when identifying an object at step 406.The points defining an identified object in a scene determined at step406 are mapped to corresponding points on a selected database object towhich the identified object is mapped at step 408. The relative matchbetween the selected points in the source content and the selectedpoints on the corresponding database object facilitates automaticrescaling and alignment of the 3D database object with the 2D view ofthe object in the source content. The match of orientation and positionin the 2D view and the 3D object is in many cases approximate and mayvisually be confirmed for accuracy with the use of a semi-transparentoverlay of the source image onto the camera view of the estimated 3Denvironment. Although placement may be completely automated, in somecases, adjustments in the placement may be made by a human operator ifnecessary and depending on the accuracy of the automated placement. Insome cases, an object may be auto-adjusted to snap its bottom mostsurface to a surface it should be resting on (e.g., a table, shelf,floor, etc.) if it is not floating in the air.

Alignment performed in the aforementioned manner is helpful forconfirming camera settings and camera pose for cases in which themovement of a camera in a cut shot is insufficient to estimate thoseparameters. Without movement, the combination of four (or some otherprescribed number of) non-coplanar points on a reference object in thedatabase and their correspondence to an object in the scene issufficient to provide a reasonable estimate of the camera pose andsettings.

FIG. 6 illustrates an example of overlaying a projection of referenceddatabase objects on the camera view of the source scene. In thisexample, the objects approximately, but not perfectly, align. FIGS. 7A-Cillustrate examples of overlay methods. FIG. 7A illustrates an overlaymethod in which the camera view is 50% transparent. FIG. 7B illustratesan overlay method comprising the difference between the camera and sceneviews. FIG. 7C illustrates an overlay method comprising a multiplicationbetween the scene and edges in the camera view. Not shown in the givenfigures is simply turning the camera view off and on, which may also bean option.

An object specification in the database comprises a variety ofinformation that defines the object, including data related to geometryand appearance. For example, a database object specification maycomprise a mesh (which may or may not be parameterized) of vertices ofthe object and their connectivities as well as normals to the polygonscomprising the surfaces defined by the vertices. Furthermore, a databaseobject specification may comprise one or more textures that describevisual details of the object surfaces and/or images of the objectsurfaces. In various embodiments, such textures may comprise images ofthe surfaces and/or specifications of parameters that define manners inwhich the textures may be procedurally generated, i.e., createdautomatically by a mathematical model rather than by direct applicationof an image. Moreover, a database object specification may comprise abump map that describes the roughness of the object surfaces. In variousembodiments, such data may be in the form of vector orientationsrelative to the normal directions of the surfaces of the object and/ormay be procedurally generated rather than being tabulated. A databaseobject specification may further comprise a model of the opticalbehavior of the object at various points on its surfaces and/orinformation that describes the desired interaction between its surfacesand incident light rays. In various embodiments, such an opticalbehavior model may range from simple global behavior such as specularreflection (mirror-like response to light), Lambertian diffusion(equally diffuse reflection of light in all directions like paper), acombination of both, or more complex behaviors such as modeling skin ortranslucent materials where some of the light passes through, bouncesaround inside, and emerges from another location on the surface and/orobject.

Once an object in a scene is initially identified at step 406 and ismapped to a corresponding database object at step 408, features of theobject in the source video may be tracked during motion of the object,and the correspondence with the database object as well as cameraposition/pose may be maintained for reasonable motions, subject toconstraints defined by the physics of the scene. For example, theorientation and position of the database object may be estimated throughthe motion of the tracked points or features of the object in the scene,with the orientation and position being adjusted based on physicalconstraints such as the object staying connected to a surface it isresting on.

Although the foregoing embodiments have been described in some detailfor purposes of clarity of understanding, the invention is not limitedto the details provided. There are many alternative ways of implementingthe invention. The disclosed embodiments are illustrative and notrestrictive.

What is claimed is:
 1. A method, comprising: identifying objects incamera captured source content; mapping each identified object to acorresponding existing database object including by mapping a prescribednumber of non-coplanar points of each identified object to correspondingpoints of the corresponding existing database object; and virtualizingthe source content by representing the source content completely usingexisting database objects to which the identified objects are mapped;wherein the virtualized version of the source content comprises are-imagined version of the source content that does not include anyoriginal camera captured pixel information.
 2. The method of claim 1,further comprising determining camera pose with respect to the sourcecontent and providing the same camera pose with respect to thevirtualized version of the source content.
 3. The method of claim 1,wherein identifying objects is at least in part automated, at least isin part assisted by human intervention, or both.
 4. The method of claim1, wherein mapping is at least in part automated, at least in partassisted by human intervention, or both.
 5. The method of claim 1,wherein mapping comprises selecting an existing database object thatbest matches an identified object.
 6. The method of claim 1, whereinmapping comprises purposely selecting a mismatching existing databaseobject for an identified object.
 7. The method of claim 1, whereinmapping comprises mapping an identified object that comprises a partialobject to a whole or complete existing database object.
 8. The method ofclaim 1, further comprising tracking and mapping motion of an identifiedobject to motion of the corresponding existing database object in thevirtualized version of the source content.
 9. The method of claim 1,wherein the source content is two-dimensional and the virtualizedversion of the source content is three-dimensional.
 10. The method ofclaim 1, wherein the source content comprises source video content. 11.A system, comprising: a processor configured to: identify objects incamera captured source content; map each identified object to acorresponding existing database object including by mapping a prescribednumber of non-coplanar points of each identified object to correspondingpoints of the corresponding existing database object; and virtualize thesource content by representing the source content completely usingexisting database objects to which the identified objects are mapped,wherein the virtualized version of the source content comprises are-imagined version of the source content that does not include anyoriginal camera captured pixel information; and a memory coupled to theprocessor and configured to provide the processor with instructions. 12.The system of claim 11, wherein the processor is further configured todetermine camera is pose with respect to the source content and providethe same camera pose with respect to the virtualized version of thesource content.
 13. The system of claim 11, wherein the processor isfurther configured to receive human input for identifying objects,mapping objects, or both.
 14. The system of claim 11, wherein to mapcomprises to select an existing database object that best matches anidentified object.
 15. The system of claim 11, wherein to map comprisesto purposely select a mismatching existing database object for anidentified object.
 16. The system of claim 11, wherein to map comprisesto map an identified object that comprises a partial object to a wholeor complete existing database object.
 17. The system of claim 11,wherein the processor if further configured to track and map motion ofan identified object to motion of the corresponding existing databaseobject in the virtualized version of the source content.
 18. The systemof claim 11, wherein the source content is two-dimensional and thevirtualized version of the source content is three-dimensional.
 19. Thesystem of claim 11, wherein the source content comprises source videocontent.
 20. A computer program product embodied in a non-transitorycomputer usable storage medium, comprising computer instructions for:identifying objects in camera captured source content; mapping eachidentified object to a corresponding existing database object includingby mapping a prescribed number of non-coplanar points of each identifiedobject to corresponding points of the corresponding existing databaseobject; and virtualizing the source content by representing the sourcecontent completely using existing database objects to which theidentified objects are mapped; wherein the virtualized version of thesource content comprises a re-imagined version of the source contentthat does not include any original camera captured pixel information.